Database Reference
In-Depth Information
Factor Analysis
: factor analysis is basically targeted at describing the relation
among many indicators or elements with only a few factors, i.e., grouping several
closely related variables and then every group of variables becomes a factor
(called a factor because it is unobservable, i.e., not a specific variable), and the
few factors are then used to reveal the most valuable information of the original
data.
Correlation Analysis
: correlation analysis is an analytical method for deter-
mining the law of correlations among observed phenomena and accordingly
conducting forecast and control. There are a plentiful of quantitative relations
among observed phenomena such as correlation, correlative dependence, and
mutual restriction. Such relations may be classified into two types: (a) function,
reflecting the strict dependence relationship among phenomena, which is also
called a definitive dependence relationship, among which, every numerical value
of a variable corresponds to one or several determined values; (b) correlation,
under which some undetermined and inexact dependence relations exist, and a
numerical value of a variable may correspond to several numerical values of the
other variable, and such numerical values present regular fluctuation surrounding
their mean values. A classic example is that customers of many supermarkets
purchase beers while they are buying diapers.
Regression Analysis
: regression analysis is a mathematical tool for revealing
correlations between one variable and several other variables. Based on a
group of experiments or observed data, regression analysis identifies dependence
relationships among variables hidden by randomness. Regression analysis may
change complex and undetermined correlations among variables into simple and
regular correlations.
A/B Testing
: also called bucket testing. It is a technology for determining plans
to improve target variables by comparing the tested group. Big data will require
a large number of tests to be executed and analyzed, to ensure sufficient scale of
the groups for detecting the significant differences between the control group and
the treatment group.
Statistical Analysis
: Statistical analysis is based on the statistical theory, a branch
of applied mathematics. In statistical theory, randomness and uncertainty are
modeled with Probability Theory. Statistical analysis can provide description and
inference for large-scale datasets. Descriptive statistical analysis can summarize
and describe datasets and inferential statistical analysis draws conclusions from
data subject to random variations. Analytical technologies based on complex
multi-variate statistical analysis include regression analysis, factor analysis,
clustering, and recognition analysis, etc. Statistical analysis is widely applied
in the economic and medical care fields [
1
].
Data Mining
: Data mining is a process for extracting hidden, unknown, but
potentially useful information and knowledge from massive, incomplete, noisy,
fuzzy, and random data. There are also some terms similar to data mining, e.g.,
discovering knowledge from databases, data analysis, data fusion, and decision
support.
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